According to a 2019 Gartner survey of three thousand CIOs across a variety of industries, the number of enterprises that utilize AI technology has grown roughly 230% over the past four years. These companies, however, may find themselves playing catch up with competitors who have found creative ways to leverage this technology within their specific industries. Just take a look at these 10 trailblazing companies who are teaching us the value of modern technical innovation through the fascinating ways that they use AI! It has been nearly 60 years since the publication of Rachel Carlson’s Silent Spring, which decried the indiscriminate use of pesticides and herbicides in commercial agriculture. Though we have seen different chemical and policy changes attempt to address the issues raised in the text, none may be as revolutionary as Blue River Tech’s AI driven farming equipment. Founded in 2011, the company has since been incorporated by leading agricultural machinery company, John Deere, in an effort to grow its flagship “See & Spray” product.
This device uses deep learning algorithms, similar to those used in facial recognition software, to discern weeds from crops, and apply appropriate amounts of chemical herbicides. As the ways that audiences consume film advertisements continues to fragment by the day, the cost for marketing films– one of the largest areas of investment for production companies– is growing exponentially. However, this troubling news comes with a silver lining. According to 2018 data compiled by IBIS Worldwide, the compound annual growth rate for the American film industry is roughly 2%, which outpaces the nation’s overall economic growth. This saddles production companies with the task of finding new ways to optimize ROI at a time where there has never been so much money to make, but where it also requires such a financial investment to make a movie. This is where Cinalytics steps in. The LA based startup’s AI powered platform considers fifteen unique attributes that can predict the potential success of a film. Not looking to remove the human element from filmmaking, CEO Tobias Queisser believes his company’s tool can “supplement the creative process”, and help producers think through artistic decisions with the added confidence provided by predictive data.
According to leading educational non-profit Autism Speaks, 1 in every 59 children born today will fall somewhere on the autism spectrum. Although symptoms are diverse, many individuals with this diagnosis struggle with typical social conventions in a manner that impacts their daily lives. As autism is typically diagnosed in early childhood, many families are tasked with the responsibility of helping their children navigate a world that better accommodates neurotypical behaviors. Brain Power is using transformative technologies, including AI and AR (augmented reality) to provide autistic children, and adults, with learning experiences that help them with daily tasks and life skills. Having consulted a diverse array of families, healthcare providers, and counselors, and ran clinical trials, the company has created software that allows users to build skills such as identifying emotions based on facial cues, and maintaining eye contact. Utilizing Google Glass, the software’s zero UI interface reacts to the user’s eye motion while simultaneously reading their environment to detect not only the presence of other people, but also another person’s facial expressions. Bias mitigation is a significant concern in modern policing.
When many think about AI implementation in police and judicial processes, they shudder at the potential risks. Because AI/ML relies on prior data, some suggest that implementing AI software will increase bias given the historical over-policing of certain groups in America. Stanford’s Computational Policy Lab recently partnered with the San Francisco Police Department to develop a tool that strips arrest records of information that might evoke such conscious or unconscious biases when being presented to the District Attorney’s Office. The software uses name-entity recognition technology to identify and remove not only an arrestee’s race, but also descriptive factors that might allude to a suspect’s demographic information from unstructured text. These include physical descriptions like eye, and hair color, names, locations, and neighborhoods where the subject lives or was arrested. The software will also remove information alluding to the identity of involved officers, including names, and badge numbers to even further prevent the DA’s office from making unfair inferences.
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